Understanding Transport Variability of the Antarctic Circumpolar Current Using Ocean Bottom ...

61 Pages·2015·5.17 MB·English

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USF Tampa Graduate Theses and Dissertations USF Graduate Theses and Dissertations
January 2013
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Jessica Makowski
University of South Florida, [email protected]
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Understanding Transport Variability of the Antarctic Circumpolar Current Using
Ocean Bottom Pressure
by
Jessica K. Makowski
A thesis submitted in partial fulfillment
of the requirements for the degree of
Master of Science
Department of Physical Oceanography
College of Marine Science
University of South Florida
Major Professor: Don P. Chambers, Ph.D.
Gary Mitchum, Ph.D.
Sang-Ik Shin, Ph.D.
Date of Approval:
October 21, 2013
Keywords: Antarctic Circumpolar Current, Southern Ocean, GRACE, Ocean
Bottom Pressure, Ocean Transport
Copyright (cid:13)c 2013, Jessica K. Makowski
Dedication
To one of the strongest and most independent women I had the pleasure to learn
from. Thank you for being one of the reasons I am who I am today and for always
encouraging me to pursue my goals. Wish you were here to celebrate with me. Love
to you, a bushel and a peck.
Acknowledgments
Manythankstomycommitteemembers, DonChambers, GaryMitchum, andSang-Ik
Shin, fortheirsupportandsuggestionswithmyresearch. Especiallymyadvisor, Don,
who took a chance bringing in a biologist and somehow turned me into a physicist.
Many thanks to all those involved in my project and research (even in the smallest
of ways), your assistance and guidance when working through problems, especially
programming, are very much appreciated. To all my friends for their patience and
encouragement, for being there through tough times, and continuing to support my
stubborn goal of completing graduate school. Finally, to my family, through all of the
trials and tribulations we’ve had these last three years, I would be nowhere without
you all.
Contents
List of Tables ii
List of Figures iii
Abstract v
1 Introduction 1
1.1 Dynamics of the Antarctic Circumpolar
Current . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.1.1 Calculating Transport Variability . . . . . . . . . . . . . . . . 7
1.2 The Gravity Recovery and Climate
Experiment (GRACE) . . . . . . . . . . . . . . . . . . . . . . . . . . 12
1.3 Research Project Summary . . . . . . . . . . . . . . . . . . . . . . . . 16
2 Assessing Uncertainty of Transport
Computed from GRACE 19
2.1 Data Sets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.1 Ocean Model . . . . . . . . . . . . . . . . . . . . . . . . . . . 20
2.1.2 GRACE Simulated Data Set . . . . . . . . . . . . . . . . . . . 21
2.1.3 Depth Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.2 Results and Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . 23
2.3 Conclusions from Statistical Testing of
Simulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
3 Evaluation of ACC Transport Variability
and Southern Hemisphere Winds 32
3.1 Transport Variability from GRACE OBP Data . . . . . . . . . . . . . 32
3.2 Comparison of Transport Variability and the Southern Annular Mode
(SAM) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36
3.3 Analysis of Australia-Antarctica Transport and Southern Ocean Winds 39
4 Conclusions and Future Work 47
5 References 49
i
List of Tables
Table 1.1 Mean transports of the four major fronts in the ACC . . . . . . 4
Table 3.1 Correlations between transport (ECCO and GRACE) and the
two SAM indices (JISAO and NERC) . . . . . . . . . . . . . . 38
ii
List of Figures
Figure 1.1 Map of front positions from Orsi et al. (1995) . . . . . . . . . . 3
Figure 1.2 Map of the percent variance of transport through Drake Pas-
sage as explained by ocean bottom pressure. . . . . . . . . . . . 10
Figure 1.3 Time series of bottom pressure recorder data in Drake Passage. 11
Figure 1.4 Time series of model data and in situ data. . . . . . . . . . . . 11
Figure 1.5 Map of the percent of transport of ACC as explained by zonal
transport . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Figure 2.1 Map of mean total transport for each 1◦ grid from 2003-2010
calculated using JPL ECCO currents at depth . . . . . . . . . . 20
Figure 2.2 Flow chart of how GRACE-like simulation data set is developed 22
Figure 2.3 Comparison of GRACE and GRACE-simulated contour maps . 24
Figure 2.4 TimeseriesoftransportcalculatedfromGRACE-likeandJPL -
ECCO data for a single transect at 140◦E . . . . . . . . . . . . 26
Figure 2.5 MeanstandarddeviationofECCOandtheGRACE-likeresid-
uals relative to the ECCO signal compared to spatial average
width . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Figure 2.6 Mean correlation of GRACE-like and ECCO for each spatial
averaging area. . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Figure 2.7 Root mean square of amplitudes from least squares estimation
for each spatial averaging area. . . . . . . . . . . . . . . . . . . 28
Figure 2.8 Root mean square of trends from least squares estimation for
each spatial averaging area. . . . . . . . . . . . . . . . . . . . . 29
Figure 2.9 Mean correlation of GRACE-like and ECCO for each spatial
averaging area, after temporal smoothing . . . . . . . . . . . . . 30
iii
Figure 2.10 Mean standard deviation of GRACE-like - ECCO residuals for
each spatial averaging area after temporal smoothing. . . . . . 31
Figure 3.1 Time series of transport variability averaged between 125◦E-
155◦E using GRACE OBP and ECCO OBP. . . . . . . . . . . . 34
Figure 3.2 Time series of low-frequency transport variability averaged be-
tween 125◦E-155◦E using GRACE OBP and ECCO OBP. . . . 35
Figure 3.3 Time series of low-frequency transport variability averaged
between 125◦E-155◦E using GRACE OBP and ECCO OBP
compared to residuals between GRACE and ECCO calculated
transport. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35
Figure 3.4 Time series of low-frequency, with trend removed transport
variability averaged between 125◦E-155◦E using GRACE OBP
and time series of low-frequency, with trend removed Southern
Annular Mode from JISAO and NERC. . . . . . . . . . . . . . 38
Figure 3.5 Map of correlation between zonal wind stress and transport. . . 39
Figure 3.6 Map of correlation between low-frequency zonal wind stress
and low-frequency transport. . . . . . . . . . . . . . . . . . . . 40
Figure 3.7 Map of correlation between wind stress curl and transport. . . . 41
Figure 3.8 Map of correlation between low-frequency wind stress curl and
low-frequency transport. . . . . . . . . . . . . . . . . . . . . . . 42
Figure 3.9 Time series of low-frequency wind stress curl north of the STF
and low-frequency transport. . . . . . . . . . . . . . . . . . . . 42
Figure 3.10 Time series of low-frequency wind stress curl south of the STF
and low-frequency transport. . . . . . . . . . . . . . . . . . . . 43
Figure 3.11 Time series of low-frequency wind stress curl difference be-
tween north and south of the STF and low-frequency transport. 44
Figure 3.12 Timeseriesoflow-frequencytransportcalculatedfromGRACE
and ECCO OBP east and west of the area of the wind stress
curl gradient. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46
iv
Abstract
Previous studies have suggested that ocean bottom pressure (OBP) can be
used to measure the transport variability of the Antarctic Circumpolar Current
(ACC). The OBP observations from the Gravity Recovery and Climate Experiment
(GRACE) are used to calculate transport along the choke point between Antarctica
and Australia. Statistical analysis will be conducted to determine the uncertainty of
the GRACE observations using a simulated data set.
There has been some evidence to suggest that Southern Hemisphere winds and
the Southern Annular Mode (SAM) or the Antarctic Oscillation (AAO) play a signif-
icant role in accelerating/decelerating ACC transport, along with some contribution
from buoyancy forcing. We will examine whether average zonal wind stress, wind
stress curl, local zonal winds, or the SAM are representative of the low frequency
zonal mass transport variability.
Preliminary studies suggest that seasonal variation in transport across the
Australia-Antarctica choke point is driven by winds along and north of the northern
front of the ACC, the Sub Tropical front (STF). It also appears that interannual vari-
ations in transport are related to wind variations centered south of the Sub Antarctic
Front (SAF). We have observed a strong negative correlation/positive correlation
across the STF of the ACC in the Indian Ocean, which suggests wind stress curl may
also be responsible for transport variations.
v
Chapter 1
Introduction
The Southern Ocean has long been an under-sampled region of the world’s oceans,
due to its remote location. However, its dynamics play a significant role in the merid-
ional overturning circulation, global climate system, deep water formation, and the
carbon cycle (Meredith et al., 2011). The Antarctic Circumpolar Current (ACC) is
the defining oceanographic feature of this important ocean basin: it is the only ocean
current that is not constrained by any land mass to the east at the Drake Passage and
it has measurable currents to the bottom. There are still many unanswered questions
concerning the ACC transport. For example, the amplitude and dominant periodici-
ties of the ACC transport are largely unknown, especially at periods longer than one
year, and it is unclear how the ACC will be affected by a changing global climate.
These questions remain in large part due to the difficulties inherent in sampling polar
regions. Strong seasonality and harsh ocean conditions have limited most of the in
situ observations of major physical parameters (e.g., temperature, salinity, velocity,
ocean bottom pressure, sea level) to a few warm season transects across the Drake
PassageandsouthofAustraliaandinotherareaswithmoreinfrequentmeasurements
(e.g., Rintoul and Sokolov, 2001; Cunningham et al., 2003; B¨oning et al., 2008). From
these data it has been shown that the ACC is comprised of three to four major fronts
(depending on the location) and that the mean transport of the ACC can be de-
termined to within 10-15 Sverdrup (Sv, 1 Sv = 106 m3/s) (e.g., Cunningham et al.,
1